光谱学与光谱分析 |
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Estimation of Winter Wheat Leaf Nitrogen Accumulation using Machine Learning Algorithm and Visible Spectral |
CUI Ri-xian1, LIU Ya-dong1, FU Jin-dong2* |
1. College of Agronomy and Plant Protection, Qingdao Agricultural University, Shandong Provincial Key Laboratory of Dryland Farming Techniques, Qingdao 266109, China 2. Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China |
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Abstract In order to study the feasibility of using digital image analysis and machine learning algorithm to estimate leaf nitrogen accumulation (LNA) of winter wheat at canopy level, digital images of winter wheat canopies grown under six levels of nitrogen application rate were taken for four times during the elongation stage. Meanwhile, wheat plants were sampled to measure LNA. The random forest method using CIEL*a*b* components was used to segment wheat plant from soil background and then extract canopy cover, RGB components of sRGB color space and compute five color indices derived from RGB components. Correlation analysis was carried out to identify the relationship between LNA and canopy cover (CC), RGB components, and five color indices. Two kinds of nonlinear least squares regression models (NLS) with different independent variables of color components and color indices, and three machine learning algorithmic of artificial neural network (ANN), support vector regression (SVR), and random forests method (RF) were used to estimate winter wheat leaf nitrogen accumulation. All three machine learning algorithm had four input variables of CC, R, G, and B. The results showed that, CC, R and G component of sRGB color space, and five color indices derived from RGB components showed significant correlations with LNA during the elongation stage. CC revealed the highest correlation with LNA. The lowest accuracy in estimation LNA was achieved by using nonlinear least square model with CC and color indices, and RF had showed the problem of overfitting. The other three methods of LNA with CC and RGB components, ANN, and SVR had showed good performance with higher R2 (0.851, 0.845, and 0.862) and lower RMSE (19.440, 19.820, and 18.698) for model calibration and validation, revealing good generalization ability.
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Received: 2015-04-02
Accepted: 2015-08-18
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Corresponding Authors:
FU Jin-dong
E-mail: fujindong@caas.cn
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[1] Rockstm J, Steffen W, Noone K, et al. Ecology & Society, 2009, 14(2): 32. [2] Muoz-Huerta R F, Guevara-Gonzalez R G, Contreras-Medina L M, et al. Sensors, 2013, 13(8): 10823. [3] Lee K J, Lee B W. European Journal of Agronomy, 2013, 48: 57. [4] Li Y, Chen D, Walker C N, et al. Field Crops Research, 2010, 118(3): 221. [5] Jia B, He H, Ma F, et al. The Scientific World Journal, 2014. 2014: doi: 10.1155/2014/602647. [6] Tewari V K, Arudra A K, Kumar S P, et al. Agricultural Engineering International: CIGR Journal, 2013, 15(2): 78. [7] ZHANG Li-zhou, WANG Dian-wu, ZHANG Yu-ming, et al(张立周, 王殿武, 张玉铭, 等). Chinese Journal of Eco-Agriculture(中国生态农业学报), 2010, 18 (6): 1340. [8] CHEN Ji-shan, ZHU Rui-fen, GAO Chao, et al(陈积山, 朱瑞芬, 高 超, 等). Acta Agrestia Sinica(草地学报), 2013, 21(3): 576. [9] Breiman L. Statistical Science, 2001, 16(3): 199. [10] WANG Da-cheng, WANG Ji-hua, JIN Ning, et al(王大成,王纪华,靳 宁,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2008, 24(S2): 196. [11] XIA Tian, WU Wen-bin, ZHOU Qing-bo, et al(夏 天,吴文斌,周清波,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2013, 29(3): 139. [12] LIANG Liang, YANG Min-hua, ZHANG Lian-peng, et al(梁 亮,杨敏华,张连蓬,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2012, 28(20): 162,294. [13] LIANG Dong, GUAN Qing-song, HUANG Wen-jiang, et al(梁 栋,管青松,黄文江,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2013, 29(7): 117. [14] LI Feng, YIN Wei-wei(李 峰,印蔚蔚). Computer Engineering(计算机工程),2011,37(17): 211. [15] Liaw A, Wiener M R. News, 2002, 2: 18. [16] WANG Yuan, WANG De-jian, ZHANG Gang, et al(王 远,王德建,张 刚,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2012,28(17): 131. [17] Adamsen F G, Pinter P J, Barnes E M, et al. Crop Science, 1999, 39(3): 719. [18] Woebbecke D M, Meyer G E, Von Bargen K, et al. Transactions of the ASAE, 1995, 38(1): 259. [19] ZHANG Li-zhou, HOU Xiao-yu, ZHANG Yu-ming, et al(张立周, 侯晓宇, 张玉铭, 等). Chinese Journal of Eco-Agriculture(中国生态农业学报), 2011, 19(5): 1168. [20] Kuhn M, Johnson K. Applied Predictive Modeling. New York: Springer, 2013. [21] Ritz C, Streibig J C. Nonlinear regression with R. Springer, 2008. [22] Venables W N, Ripley B D. Modern Applied Statistics with S. 4th ed. New York: Springer, 2002. [23] Karatzoglou A, Smola Am, Hornik K, et al. Journal of Statistical Software, 2004, 11(9): 1. [24] James G, Witten D, Hastie T, et al. An Introduction to Statistical Learning. New York: Springer, 2013. [25] Hastie T, Tibshirani R, Friedman J, et al. The Elements of Statistical Learning. New York: Springer, 2009. [26] WU Xi-zhi(吴喜之). Statistics(统计学). Beijing: Higher Education Press(北京: 高等教育出版社), 2008. [27] LIU Ji-ping (刘吉平). Remote Sensing Principle and Remote Sensing Information Analysis(遥感原理及遥感信息分析基础). Wuhan: Wuhan University Press(武汉: 武汉大学出版社), 2012. |
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